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Introduction

Ask the chat agent from Part 1 what the team runbook prescribes for an SSH brute force against web-server-01, or ask the MCP sidecar from Part 2 whether a file hash appeared in a threat intelligence feed last month, and the answer is whatever Claude remembers about the world in general. Both tools query alerts well. Neither of them can read a document, and the runbook, the manual and the feed never reach the model.

Introduction

In Part 1 we connected AWS Bedrock Claude to the Wazuh Dashboard chat via ML Commons. That approach works well for analysts working inside the Wazuh UI. In this part we open a second channel: Model Context Protocol (MCP), which allows any compatible client - Claude Desktop, custom applications, CI pipelines - to query Wazuh Indexer data through a standardized tool interface.

Introduction

In the previous article we embedded a local Ollama model directly into the Wazuh Dashboard chat via ML Commons. That approach provides full control over data with no cloud dependencies. In this series we take a parallel path: using AWS Bedrock - specifically Claude Sonnet 4.5 - as the inference backend, while all security data stays strictly within the local Docker network.

Introduction

Integrating local language models directly into the Wazuh interface opens fundamentally new capabilities for information security teams. Unlike cloud-based AI solutions, Ollama enables security event analysis entirely within an organization’s isolated infrastructure, eliminating the transmission of confidential data beyond the network perimeter. Embedding an AI assistant into the Wazuh dashboard provides SOC analysts with instant access to intelligent alert interpretation, automatic incident correlation, and response recommendation generation directly within the workflow context. This approach significantly reduces the time required for initial threat analysis and decreases the cognitive load on specialists, allowing them to focus on strategic decision-making instead of routine event processing. Meanwhile, full control over the model and data remains within the organization, which is critically important for regulatory compliance and internal security policies.

When Your SOC Analyst Can’t Keep Up (Or Just Needs a Break)

Let’s be honest: analyzing thousands of security events every day isn’t the most exciting job.

Introducing Wazuh LLM: Why Specialized Security Analysis Matters

In the cybersecurity world, SOC specialists deal with massive streams of security events daily. Analyzing each alert requires deep knowledge, experience, and time. That’s why I created a specialized language model to assist security analysts in their day-to-day operations.

Related Reading:

• Wazuh Integration with Ollama Series - Learn how to integrate Wazuh with Ollama
• Wazuh LLM Security Event Analysis - Specialized model for Wazuh events

Prerequisites and Environment Setup

For local RAG development, ensure you have the following requirements:

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a method that allows the use of information from various sources to generate more accurate and useful responses to questions.

Continuing the Series: Integrating a Wazuh Cluster with Ollama - Part 4. Configuration and Implementation

Related: Check out our Wazuh LLM fine-tuned model for specialized security event analysis.

Wazuh and Ollama: Part 3. Creating Integration Between Your Wazuh Cluster and Ollama

Wazuh offers vast and nearly limitless possibilities for integration with various systems. Even if a specific feature is missing, you can always create your own custom integration.